Characterization of the kidney transcriptome of the South American olive mouse Abrothrix olivacea.

backgroundThe olive mouse Abrothrix olivacea is a cricetid rodent of the subfamily Sigmodontinae that inhabits a wide range of contrasting environments in southern South America, from aridlands to temperate rainforests. Along its distribution, it presents different geographic forms that make the olive mouse a good focal case for the study of geographical variation in response to environmental variation. We chose to characterize the kidney transcriptome because this organ has been shown to be associated with multiple physiological processes, including water reabsorption.

resultsTranscriptomes of thirteen kidneys from individuals from Argentina and Chile were sequenced using Illumina technology in order to obtain a kidney reference transcriptome. After combining the reads produced for each sample, we explored three assembly strategies to obtain the best reconstruction of transcripts, TrinityNorm and DigiNorm, which include its own normalization algorithms for redundant reads removal, and Multireads, which simply consist on the assembly of the joined reads. We found that Multireads strategy produces a less fragmented assembly than normalization algorithms but recovers fewer number of genes. In general, about 15000 genes were annotated, of which almost half had at least one coding sequence reconstructed at 99% of its length. We also built a list of highly expressed genes, of which several are involved in water conservation under laboratory conditions using mouse models.

conclusionBased on our assembly results, Trinity's in silico normalization is the best algorithm in terms of cost-benefit returns; however, our results also indicate that normalization should be avoided if complete or nearly complete coding sequences of genes are desired. Given that this work is the first to characterize the transcriptome of any member of Sigmodontinae, a subfamily of cricetid rodents with about 400 living species, it will provide valuable resources for future ecological and evolutionary genomic analyses.